Prevalence and associated factors of insomnia symptoms during the COVID-19 pandemic lockdown among Mettu town residents

Background Insomnia is a prevalent sleep disorder that affects people all over the world. Creating suitable interventions will require a better understanding of the magnitude and determinants of insomnia. This study aimed to assess the prevalence and associated factors of insomnia symptoms among residents of Mettu town during the pandemic lockdown. Methodology A community-based cross-sectional study was conducted among residents of Mettu town from October 1st to October 15th, 2020. Residents who lived in Mettu town at least for six months were included. To determine the prevalence and determinants of insomnia symptoms, both descriptive and inferential analyses were used. The chi-squared test of association and logistic regression was used to identify predictors of insomnia symptoms among residents of Mettu town. We used SPSS version 25 for all statistical analyses. Principal findings The prevalence of depressive symptoms among residents of Mettu town was 52.6%. According to results of multivariable binary logistic regression, being female [AOR = 3.677, 95%CI: 2.124–6.365], being aged between 19 and 40 [AOR = 13.261, 95%CI: 6.953–25.291], being aged above 41 [AOR = 2.627, 95%CI: 1.120–6.159], smoking [AOR = 15.539, 95%CI: 7.961–30.329], satisfaction with information available [AOR = 0.310, 95%CI: 0.168–0.570], fear Corona Virus Disease 2019 (COVID-19), [AOR = 2.171, 95%CI: 1.262–3.733], feeling alienated from others [AOR = 3.288, 95%CI: 1.897–5.699], having somatic symptoms [AOR = 2.298, 95% CI: 1.360–3.884], having depressive symptoms [AOR = 1.841, 95% CI: 1.073–3.160], and experiencing psychological distress [AOR = 1.962, 95% CI: 1.173–3.281] were significantly associated with insomnia symptoms. Conclusion In this study, the prevalence of insomnia symptoms was found to be high among residents of Mettu town. Being female, being aged between 19 and 40, being aged above 41 years, smoking, fear of Corona Virus Disease 2019, feeling alienated from others, having somatic symptoms, having depressive symptoms, and experiencing psychological distress were all associated with an increased risk of developing insomnia symptoms while being satisfied with the information available decreased the risk of insomnia symptoms among residents of Mettu town. Interventions should be put in place to promote healthy sleep among residents of Mettu town.

Insomnia is a serious health problem with symptoms such as difficulty falling asleep, staying asleep, and waking up early in the morning, and it has become a major public health issue around the world [19,20]. It is also linked to daytime exhaustion, decreased activity, absenteeism, low quality of life, and major medical and societal expenditures [21,22]. Previous studies have found that insomnia can induce diseases such as hypertension, diabetes, and cardiovascular disease [23]. It also contributes to psychological disorders such as anxiety, sadness, bipolar disorder, and suicidal ideation [24,25]. Because insomnia has serious effects, including depression, poor work performance, and overall poor quality of life [26], examining the prevalence and associated characteristics of insomnia is critical for developing prevention, interventions, and allocating health resources. However, research on sleep problems like insomnia in developing countries including Ethiopia is scarce. Therefore, the primary goal of the study was to measure the prevalence of insomnia in the Mettu town population. Our secondary interest was identifying factors associated with insomnia among Mettu town residents, in southwest Ethiopia.

Study design and setting
From October 1 st to October 15 th , 2020, a community-based cross-sectional study was conducted on residents of Mettu Town in Southwestern Ethiopia. A self-administered questionnaire was used to collect data from town residents chosen using a simple random sampling technique. Residents must have lived in Mettu for at least six months to be included in our sample. Participants who were unable to provide information were excluded from the study.
Sampling procedure. The sample size was calculated using a single population proportion formula, and a simple random sampling technique. The sample size needed for the study was calculated by assuming 50% of the prevalence of insomnia among residents in a single population fraction for unknown prevalence, with a margin of error of 4%, and a non-response rate of 10%. Then the sample size for this study becomes 600.25+60�661.
Study variables. The response variable was insomnia symptoms (yi), which is dichotomized as yi ¼ 1; presence of insomnia symptoms 0; otherwise ( Explanatory variables: Demographic variables (gender, age (in years), education level, alcohol consumption, chewing Khat, smoking status). COVID-19-related perception (COVID-19 infection, fear of COVID-19, anyone around with confirmed or suspected of COVID-19 infection, the satisfaction of available information, feeling alienated from others), Psychological factors (depressive symptoms, somatic symptoms, and psychological distress) Data collection tools and measurements. To determine the symptoms of insomnia, the Insomnia Severity Index (ISI) was used. It is a reliable and valid instrument to quantify the severity of perceived insomnia [27]. A score greater than or equal to eight indicates the presence of insomnia symptoms [28,29].
For the evaluation of psychological distress in response to COVID-19, the Impact of Events Scale-Revised (IES-R) was performed [33]. The total scores can be categorized into four different levels on a five-point Likert scale: subclinical (0-8), mild (9-25), moderate (26-43), and severe . In the Chinese version of the IES-R, high reliability and validity were identified. The sum score � 9 on IES-R is assumed to have psychological distress symptoms [34].
Method of data analysis. To highlight descriptive results, we used frequency distribution and percentages. The chi-squared test of association was employed to examine the relationship between response and explanatory variables. The logistic regression model was utilized to find determinants of insomnia symptoms. Multivariable logistic regressions were conducted by taking all significant covariates in the Univariable analysis at a significance level of 25% [37]. The Hosmer and Lemeshow test is used for determining the model's goodness of fit. In the current study, SPSS version 25 was used for all statistical analyses [38].
Binary logistic regression. When the dependent variable is dichotomous, such as the presence or absence of a specific event, and the independent variables are of any type, binary logistic regression is used. The Bernoulli distribution for the Bernoulli trial specifies probabilities P (Y = 1) = π and P(Y = 0) = 1π, for which E (Y) = π.
The general model for binary logistic regression is as follows: Where: x i is an independent variable in the model, π: the probability of success, 1-π: the probability of failure, β o is constant terms, β i and is the coefficients/slope of the independent variable in the model.
Ethical clearance and consent to the publication. Ethical clearance was obtained from the research review committee of the College of Natural Science, Mettu University. The objective of the study was explained to the participants. Since the participants included all of the town's societies, including those who were illiterate and hesitant to give signatures or thumbprints on consent forms, verbal informed consent was obtained from all participants. In the event of minors, the home leader (father, mother, or guardians) gave oral consent first, followed by all child participants. Participants were also informed to have the right not to participate in the study if they were not interested to be involved in it.

Results
This study was carried out to assess the prevalence and predictors of insomnia symptoms among the residents of Mettu Town, southwest Ethiopia, during the COVID-19 pandemic lockdown. In this study, both descriptive and inferential analyses have been used to assess the prevalence and predictors of insomnia symptoms. The sample size of this study was 661, however, 45 questionnaires were discarded due to missing information or refusal to reply, resulting in a total of 616 participants. Of 616 study participants, 324(52.6%) experienced insomnia symptoms, while 292(47.4%) did not (Fig 1).

COVID-19-related perception and insomnia symptoms
Out of the total, 277 (45.0%) reported concern about COVID-19 infection, with 206 (74.4%) experiencing insomnia symptoms. In response to the question "if someone around with confirmed COVID-19 infection," 47 (7.6%) reported having a family member infected with COVID-19, with more than one-third (38.3%) developing insomnia symptoms. It is also found that about two-thirds of respondents (60.7%) were dissatisfied with the available information, and approximately half of 295 (47.9%) felt alienated from others ( Table 2).
The findings in (Table 2) show psychological characteristics and insomnia symptoms among study participants. Depressive symptoms, somatic symptoms, and psychological distress were present in approximately 232 (37.7 percent), 262 (42.5 percent), and 328 (53.2 percent) of the population, respectively. Insomnia symptoms were experienced by 68.1 percent, 71.4 percent, and 64.0 percent of those polled.

Univariable analysis
In the Univariable analysis, covariates with a p-value less than 25% were considered for multivariable analysis. From the Univariable analysis, we observed that the covariate gender, age (in Table 1 years), alcohol drinking habit, smoking habit, anyone around with confirmed COVID-19 infection, the satisfaction of available information, feeling alienated, depressive symptoms, somatic symptoms, and psychological distress were significant. However, chewing Khat and education level was not a significant at 25% level of significance. Therefore, based on this result, it is better to ignore this covariate and shall do our multivariable analysis using the significant factors. Hence, the effects of these significant covariates shall better be interpreted using the multivariable analysis.

Multivariable analysis
Gender, age, smoking habit, satisfaction with available information, fear of COVID-19, feeling alienated, anyone around infected with COVID-19, somatic symptoms, depressive symptoms, and psychological distress were all found to be statistically significant at the 5% level of significance in a multivariable binary logistic regression ( Another element connected with insomnia symptoms was satisfaction with available information. Those who were satisfied with the provided information were 0.310 [95% CI: 0.168-0.570] times less likely to suffer insomnia symptoms than those who were dissatisfied. Respondents who were afraid of COVID-19 had a two-fold (2.171) [95% CI: 1.262-3.733] greater  (Table 3).
Model adequacy checking. The Hosmer and Lemeshow test result is large (pvalue = 0.486), indicating that the model was a good fit for the data. Furthermore, Nagelkerke's R square (0.688) revealed that existing explanatory variables in the model explained 68.8% of the variation among response variables, while error terms and unknown factors accounted for the remaining 31.2% ( Table 3).
The Reciever operating characteristics curve (ROC curve) plots the probability of detecting true signal (sensitivity) and false signal (1-specificity) for an entire range of possible cut points. The area under the ROC curve indicates how effective the test is in a numerical sense. In the case of this study, for the fitted model (Table 1), a plot of sensitivity versus 1-specificity over all possible cut points is shown (Fig 2). The model performance is regarded as excellent if the area is 0.8�ROC�0.9, while more than 0.9 is considered outstanding. Our result showed that the area under this curve is determined by the Mann-Whitney U statistic and is 0.879. Therefore, based on the area under the curve indicates our model performance is excellent to predict the event. Furthermore, if the model is a good fit, then the absolute values of the residuals are relatively small, and the residual points will be more or less evenly dispersed about the horizontal axis. In the current study, residual values dispersed around the horizontal reference line, indicate that the model is a good fit for the data (Fig 3).

Discussion
The purpose of this study was to determine the prevalence and associated factors of insomnia symptoms among Mettu town residents during the pandemic lockdown. Understanding the factors associated with insomnia symptoms could help to provide precise interventions for insomnia in the public. Moreover, it contributes to the evidence of the effects of the COVID-19 pandemic on mental health. Besides, this is one of the few studies in developing countries that used standardized measurement tools and conducts rigorous analyses. In the current study, being female, being older, having smoking habits, satisfaction with the information available, fear of COVID-19, feeling alienated from others, having somatic symptoms, having depressive symptoms, and experiencing psychological distress were all found to be risk factors for insomnia symptoms.
Insomnia affects 10%-30% of the population worldwide, with some estimates reaching 50%-60% [26]. In the present study, the prevalence of insomnia symptoms was 52.6 percent. This is consistent with a previous report from India at 53.45% [39], and Turkey at 51.0% [40]. However, the current studies report was higher than previous reports, China's general public,   [44], systematic review and meta-analysis of 13 countries 36.0% [45], Greek 37.6% [43], and Ethiopia 42.9% [46]. Another study from the United States shows that the prevalence of insomnia symptoms in adolescents ranges between 3.4 and 34.6% [47,48]. Similarly in a group of school teachers in Portugal, 40.6% reported experiencing sleeplessness symptoms [49]. This difference might be due to different study designs, populations, and cultural differences. While some research found an even higher prevalence of Insomnia symptoms, multi-country reports among recovered COVID-19 patients revealed a prevalence of 77.6 percent [50]. This increased prevalence of insomnia symptoms could be attributed to the fact that they only included COVID-19 infected and recovered patients.

Significant factors associated with insomnia symptoms
Our study findings revealed that gender was found to be a significant predictor of insomnia symptoms among study participants. Females were more likely than males to experience insomnia symptoms. Females may be burdened by familial duties like many tasks and responsibilities, gender discrimination such as gender-based violence, and concomitant common mental illnesses such as depression and anxiety, which are more common in females than in males [51]. Previous studies [52,53] reported the same result.
According to the findings of the current study, age has a strong relationship with insomnia symptoms. Insomnia symptoms were more common in older people than in younger people. In our study, people between the ages of 19 and 40 years, as well as those above 41 years had greater insomnia symptoms than those under the age of 18. This is consistent with earlier research, which found that the rate of insomnia symptoms increased with age and that insomnia symptoms were more likely to occur in the elderly [54][55][56]. The physiological changes in sleep and circadian rhythm that occur during life might explain this phenomenon [57,58]. Additionally, stressful life events or medical problems, such as respiratory difficulties, physical impairment, and poor perceived health, enhance the incidence of sleeplessness in older individuals [58].
In our study, smokers had a higher incidence of insomnia symptoms than nonsmokers, which is consistent with previous findings [59,60]. Numerous studies have revealed that smokers are more likely to experience the symptoms of insomnia, including leg movements while sleeping more frequently, shorter sleep duration, and higher rapid eye movement [61][62][63]. Accordingly, numerous earlier studies have demonstrated a connection between smoking, especially late-night smoking, and more severe insomnia and shorter sleep duration [61][62][63]. According to research done by Sabanayagam & Shankar (2011), current smokers of cigarettes were almost twice as likely to report not getting enough rest and sleep as opposed to nonsmokers [64]. Furthermore, Andrea, et al. (2021) suggested that quitting smoking could reverse the detrimental effects of smoking on sleep [65]. As a result, sleep health should be promoted in programs to help smokers quit to reduce their chances of developing insomnia.
In line with a previous study [39], the current study revealed that people who fear COVID-19 have greater sleeplessness than those who are unconcerned. This finding is consistent with prior evidence on insomnia and COVID-19-related concerns [66]. One of the most powerful predictors of sleep disruption is worry [67]. Worrying thoughts, such as repeated thoughts, negative overthinking, cognitive arousal, and intrusive thoughts, have been linked to insomnia symptoms [68]. Worrying about unpleasant situations can also contribute to hyper-arousal, which is a major cause of insomnia [69].
Respondents' feelings of alienation were revealed to be a risk factor for the development of insomnia symptoms. According to a prior study, more acute insomnia at the baseline was associated with worse parental connections and more peer issues [70]. In line with this, in Greece, those who felt alienated were more likely to suffer from insomnia symptoms [43]. In COVID-19 isolation, Matias et al. (2020) describe human needs and suggest that protective practices that are good for one's health be encouraged [71]. In support of this, Ana Veronica Scott et al. studied participants' physical activity and analyzed its relationship with insomnia, finding that physically active participants had lower ISI scores [72]. So, to alleviate the loneliness that causes insomnia, we suggested physical activities.
A prior study found that those with confirmed or suspected family members, friends, and residents had more severe symptoms of mental health issues [73]. In line with this report, the current study revealed that the existence of a family member infected with COVID-19 was significantly associated with insomnia symptoms. This is also confirmed by a recent study [74,75], which discovered that persons having family members infected with COVID-19 had a higher risk of having sleeplessness issues. Another study discovered that one out of every five survivors' family members was diagnosed with a mental disease for the first time, including sleeplessness symptoms [76][77][78].
Satisfaction with available information was discovered to be another factor associated with insomnia symptoms. In the current study, those who were satisfied with the information provided were less likely to experience insomnia symptoms than those who were unsatisfied. This is in agreement with a previous study from China [56]. Obtaining appropriate knowledge about the virus from social and mass media may assist them in avoiding stress, and sleeplessness symptoms. Previous research [56,79] found a link between depressive symptoms, somatic symptoms, psychological distress, and the onset of insomnia symptoms [80]. In line with prior research, the current study found that depressive symptoms, somatic symptoms, and psychological distress were all substantially related to insomnia symptoms.
Various intervention strategies were used during the COVID-19 outbreak to assist patients with treating mental health issues, including insomnia. CBT (cognitive behavioral therapy) is a versatile strategy for treating a variety of mental illnesses [81]. During an outbreak, several institutions have shifted to providing online psychotherapy to those suffering from mental health disorders via video conferencing platforms to reduce viral transmission from face-toface therapy. Furthermore, providing online or smartphone-based psychoeducation regarding the virus's spread, promoting mental well-being, and initiating psychological intervention might be beneficial (e.g. cognitive behavior therapy [CBT] and mindfulness-based therapy [MBT]) [82]. In addition, digital cognitive behavioral therapy for insomnia (dCBT-I) [83], and Internet CBT (iCBT) [84] are effective therapeutic options for patients with insomnia. The effectiveness of dCBT-I in treating insomnia is supported by a meta-analysis of randomized controlled trials, and dCBT-I has the potential to revolutionize CBT-I delivery by increasing the accessibility and availability of CBT-I information for insomnia patients around the world [83].

Limitations of the study
The current study tried to assess insomnia symptoms among residents of Mettu towns, in southwest Ethiopia. There are some limitations while conducting this study. Firstly, we cannot prove a causal relationship in this cross-sectional study. As a second point, a self-reported questionnaire was conducted, which will contribute to a certain amount of answer bias. Finally, in addition to the variables we considered, there may be other factors related to the prevalence of insomnia symptoms among residents that can cause insomnia, which requires further investigation.

Conclusions
The current study found that residents had experienced a higher prevalence of insomnia symptoms during the COVID-19 pandemic. Being female, being older, smoking, having fear of COVID-19, feeling alienated from others, having somatic symptoms, having depressive symptoms, and experiencing psychological distress were all associated with an increased risk of developing insomnia symptoms, while satisfaction with information available decreased the risk of insomnia symptoms among Mettu town residents. Measures to enhance the mental health of people who had sleeplessness symptoms should be done based on significant factors and responsible bodies should endeavor to safeguard them. Interventions based on influencing factors should be implemented to ensure the sleep quality of residents.